Author name cluster

Simon Huber

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

IROS Conference 2025 Conference Paper

Budget-optimal multi-robot layout design for box sorting

Peiyu Zeng
Yijiang Huang
Simon Huber
Stelian Coros

Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system’s flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computational framework to generate a budget-minimizing layout by selectively placing stationary robots on a floor grid to sort packages from given input and output locations. Finding a good layout that minimizes the hardware budget while ensuring motion feasibility is a challenging combinatorial problem with nonconvex motion constraints. We propose a new optimization-based approach that models layout planning as a subgraph optimization problem subject to network flow constraints. Our core insight is to abstract away motion constraints from the layout optimization by precomputing a kinematic reachability graph and then extract the optimal layout on this ground graph. We validate the motion feasibility of our approach by proposing a simple task assignment and motion planning technique. We benchmark our algorithm on problems with various grid resolutions and number of outputs and show improvements in memory efficiency over a heuristic search algorithm. In addition, we demonstrate that our algorithm can be extended to handle various types of robot manipulators and conveyor belts, box payload constraints, and cost assignments.

Details

IROS Conference 2022 Conference Paper

Differentiable Collision Avoidance Using Collision Primitives

Simon Zimmermann
Matthias Busenhart
Simon Huber
Roi Poranne
Stelian Coros

A central aspect of robotic motion planning is collision avoidance, where a multitude of different approaches are currently in use. Optimization-based motion planning is one method, that often heavily relies on distance computations between robots and obstacles. These computations can easily become a bottleneck, as they do not scale well with the complexity of the robots or the environment. To improve performance, many different methods suggested to use collision primitives, i. e. simple shapes that approximate the more complex rigid bodies, and that are simpler to compute distances to and from. However, each pair of primitives requires its own specialized code, and certain pairs are known to suffer from numerical issues. In this paper, we propose an easy-to-use, unified treatment of a wide variety of primitives. We formulate distance computation as a minimization problem, which we solve iteratively. We show how to take derivatives of this minimization problem, allowing it to be seamlessly integrated into a trajectory optimization method. We demonstrate that the resulting method can be used to plan smooth and collision-free paths based on a variety of single- and multi-robot scenarios with different obstacles.

Details

ICRA Conference 2021 Conference Paper

Task Autocorrection for Immersive Teleoperation

Chenyang Wang
Simon Huber
Stelian Coros
Roi Poranne

Teleoperating robotic arms is a challenging task that requires years of training to master. It is mentally demanding, as the operator must internally compute transformations, or rely on muscle memory, to perform even the simplest tasks. Alternative methods that rely on embodiment –the immersive, first person experience of controlling the robot from its point of view are recently becoming more popular, thanks to the emergence of mixed reality devices. These methods create an intuitive experience by tracking the users motions, and retargetting them to the robot. However, even recent hardware fails at achieving total immersion, due to inherent discrepancies such as latency, imperfect tracking, and the differences between human and robot motor systems. Thus, performing even simple pick-and-place tasks with these systems, while more intuitive, is still cumbersome, and far from the level of human performance. In this paper we propose an immersive system that aims to bridge this gap. The system tracks the user’s motion and retargets them to the robot as usual, but it also detects the user’s intent, that is, the task they wish to perform. Based on this knowledge, the system can autocorrect the motion when it is about to fail, in a seamless manner, such that the task is successfully performed. We evaluate the efficacy of our autocorrection system in a user study. The results show a statistically significant performance improvement in terms of operation accuracy and time.

Details

FLAP Journal 2017 Journal Article

Cubical Type Theory: A Constructive Interpretation of the Univalence Axiom.

Cyril Cohen
Thierry Coquand
Simon Huber
Anders Mörtberg

This paper presents a type theory in which it is possible to directly manipulate n-dimensional cubes (points, lines, squares, cubes, etc.) based on an interpretation of dependent type theory in a cubical set model. This enables new ways to reason about identity types, for instance, function extensionality is directly provable in the system. Further, Voevodsky’s univalence axiom is provable in this system. We also explain an extension with some higher inductive types like the circle and propositional truncation. Finally we provide semantics for this cubical type theory in a constructive meta-theory.

Details